---
title: "Covid-19 Dashboard by Ashiq"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
vertical_layout: scroll
theme: cerulean
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(leaflet)
load("datafinal.Rdata")
load("df.Rdata")
#source("Data manipulation.R",local = T)
theme_set(ggthemes::theme_economist())
```
Overview
====================================================================
Sidebar{.sidebar}
---------------------------------------------------------------
Last updated:
```{r}
library(lubridate)
d <- Sys.Date()-1
paste(day(d),months(d),",2020")
```
_About :_
My name is **Md Asrafur Rahman Ashiq**.I am a student of the department of Statistics at University of Dhaka,Bangladesh.I tried to build this dashboard.It's my first dashboard.Any kinds of mistake should be forgiven and mentioned.
Data source: ```coronavirus``` package from Comprehensive R Archive Network(CRAN).
[Know more about me(LinkedIn)]("https://www.linkedin.com/in/md-asrafur-rahman-ashiq-5462ab1a0/")
Row
----------------------------------------------
### Worldwide Confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df$cases_new,na.rm = T), big.mark = ","), "", sep = " "),
caption = "Total Confirmed Cases",
icon = "fas fa-user-md",
color ="violet")
```
### Death {.value-box}
```{r}
valueBox(value = paste(format(sum(df$deaths_new, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$deaths_new, na.rm = TRUE) / sum(df$cases_new,na.rm = T), 1),
"%)", sep = " "),
caption = "Death Cases",
icon = "fa-heartbeat",
color = "#FF3333")
```
### Recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df$recovered_new, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$recovered_new, na.rm = TRUE) / sum(df$cases_new,na.rm = T), 1),
"%)", sep = ""),
caption = "Recovered Cases",
icon = "fa-walking",
color = "#33FFCC")
```
Row {.tabset}
------------------------------------------------------------------
### Top affected countries
```{r}
library(highcharter)
df%>%filter(location %in% df$location[1:12])%>%
hchart("column",hcaes(x=location,y=cases_new,color=location),name="Confirmed Cases",dataLabels = list(align = "center", enabled = TRUE)) %>%hc_yAxis(title=list(text="Total confirmed case")) %>%hc_xAxis(title="")%>%hc_add_theme(hc_theme_ggplot2())
```
### Datasets
```{r}
d <- df%>%select(-lat,-long) %>% rename(Cases=cases_new,
Deaths=deaths_new,
Recovered=recovered_new)
d$lat <- NULL
d %>%DT::datatable()
```
### Countries over date
```{r}
data %>% filter(location==df$location[1:10]) %>% group_by(location,date) %>%
summarise(sum=sum(value))%>% mutate(cum=cumsum(sum)) %>% hchart("line",hcaes(x=date,y=cum,group=location))%>%hc_yAxis(title=list(text="Cumulative Confirmed cases"))
```
Row {.tabset}
------------------------------------------------------------
### Top recovered countries
```{r}
r <- df %>%arrange(-recovered_new)
r %>% filter(location %in% r$location[1:12])%>%
hchart("column",hcaes(x=location,y=recovered_new,color=location),name="Recovered",dataLabels = list(align = "center", enabled = TRUE)) %>% hc_yAxis(title=list(text="Total Recovered case")) %>%hc_xAxis(title="") %>%hc_add_theme(hc_theme_ggplot2())
```
### Top countries with death
```{r}
df%>%filter(location %in% df$location[1:12])%>%
hchart("column",hcaes(x=location,y=cases_new,color=location),name="Confirmed Cases") %>%hc_yAxis(title=list(text="Total death cases")) %>%hc_xAxis(title="")
```
World Map
================================================================
Sidebar{.sidebar}
---------------------------------------------------------------
Wikipedia says,In December 2019, a pneumonia outbreak was reported in Wuhan, China.On 31 December 2019, the outbreak was traced to a novel strain of coronavirus, which was given the interim name 2019-nCoV by the World Health Organization (WHO), later renamed SARS-CoV-2 by the International Committee on Taxonomy of Viruses.
As of **15 August 2020**, there have been at least 765,029 confirmed deaths and more than 21,173,009 confirmed cases in the COVID-19 pandemic. The Wuhan strain has been identified as a new strain of Betacoronavirus from group 2B with approximately 70% genetic similarity to the SARS-CoV.[114] The virus has a 96% similarity to a bat coronavirus, so it is widely suspected to originate from bats as well. The pandemic has resulted in travel restrictions and nationwide lockdowns in many countries.
Column {data-width=650}
-----------------------------------------------------------------------
### Countries with more than 50k confirmed cases
```{r fig.height=6.4}
df %>%
filter(cases_new>50000) %>% leaflet()%>%addTiles()%>%
addProviderTiles("Esri.WorldImagery")%>%setView(0,0,zoom = 2.3)%>%
addMarkers(lat = ~lat,lng = ~long,popup = ~paste(sep=" ",location,"Confirmed Cases :",cases_new,",",
" Deaths :",deaths_new,",","Recovered :",recovered_new),
label = ~paste0(location," (Click for details)"),
labelOptions = labelOptions(textsize = "18px"),
popupOptions = popupOptions(maxWidth = 200))
```
Situation of Bangladesh
===============================================================
Row
----------------------------------------------
### Worldwide Confirmed {.value-box}
```{r}
df <- df %>% filter(location=="Bangladesh")
valueBox(value = paste(format(sum(df$cases_new,na.rm = T), big.mark = ","), "", sep = " "),
caption = "Total Confirmed Cases",
icon = "fas fa-user-md",
color ="violet")
```
### Death {.value-box}
```{r}
valueBox(value = paste(format(sum(df$deaths_new, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$deaths_new, na.rm = TRUE) / sum(df$cases_new,na.rm=T), 1),
"%)", sep = ""),
caption = "Death Cases",
icon = "fa-heartbeat",
color = "#FF3333")
```
### Recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df$recovered_new, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$recovered_new, na.rm = TRUE) / sum(df$cases_new,na.rm=T), 1),
"%)", sep = ""),
caption = "Recovered Cases",
icon = "fa-walking",
color = "#33FFCC")
```
Column
---------------------------------------------------------------
### Districts with more than 2500 confirmed cases(This map was last updated on 15 August,2020.)
```{r fig.height=5}
library(sf)
bd <- read_sf("Bangladesh data/BGD_adm2.shp")
district <- readxl::read_xlsx("Bangladesh data/COVID-19.xlsx")
#district <- district %>% mutate_geocode(District,source='dsk')
#save(district,file="lat lon district.Rdata")
load("lat lon district.Rdata")
district <- district %>% mutate(lab=ifelse(Cases>2500,paste(District,Cases),NA))
m <- ggplot(bd)+geom_sf(aes(fill=NAME_2))+
geom_point(data=district,aes(x=lon,y=lat,size=Cases),col="red")+
ggrepel::geom_label_repel(data=district,aes(x=lon,y=lat,label=as.character(lab),alpha=.5))+theme_minimal()+theme(legend.position = "")
m
```
### Overall Scenario of Bangladesh
```{r}
data$data_type <- str_replace(data$data_type,"_new","")
data %>% filter(location=="Bangladesh") %>%
group_by(data_type,date) %>%
summarise(sum_case=sum(value))%>%
mutate(cum_case=cumsum(sum_case)) %>% hchart("line",hcaes(x=date,y=cum_case,group=data_type,),dataLabels = list(align = "center", enabled = TRUE)) %>% hc_yAxis(title=list(text="Cumulative Total"))%>% hc_colors(c("#0073C2FF", "#EFC000FF","red")) %>% hc_add_theme(hc_theme_ggplot2())
```
